The manufacture and usage of photovoltaic systems has advanced significantly in recent years due to a continually growing demand for renewable energy resources. The cost per watt of electricity generated by photovoltaic systems has decreased more dramatically than in earlier systems, especially when combined with government incentives offered to encourage photovoltaic power generation. Typically, when integrated into a power grid, photovoltaic systems are collectively operated as a fleet, although the individual systems in the fleet may be deployed at different physical locations within a geographic region.
Grid connection of photovoltaic power generation fleets is a fairly recent development. A power grid is an electricity generation, transmission, and distribution infrastructure that delivers electricity from supplies to consumers. As electricity is consumed almost immediately upon production, power generation and consumption must be balanced across the entire power grid. A large power failure in one part of the grid could cause electrical current to reroute from remaining power generators over transmission lines of insufficient capacity, which creates the possibility of cascading failures and widespread power outages.
Photovoltaic fleets connected to a power grid are expected to exhibit predictable generation behaviors. Accurate production data is particularly crucial when a photovoltaic fleet makes a significant contribution to a power grid's overall energy mix. Various kinds of photovoltaic plant production forecasts can be created and combined into photovoltaic power generation fleet forecasts, such as described in commonly-assigned U.S. Pat. Nos. 8,165,811; 8,165,812; 8,165,813, all issued to Hoff on Apr. 24, 2012; U.S. Pat. Nos. 8,326,535 and 8,326,536, issued to Hoff on Dec. 4, 2012; U.S. Pat. No. 8,335,649, issued to Hoff on Dec. 18, 2012; and U.S. Pat. No. 8,437,959, issued to Hoff on May 7, 2013, the disclosures of which are incorporated by reference.
Photovoltaic production forecasting first requires choosing a type of solar resource appropriate to the form of simulation. The solar resource data is then combined with each plant's system configuration in a photovoltaic simulation model, which generates a forecast of photovoltaic production. Confusion exists about the relationship between irradiance and irradiation, their respective applicability to forecasting, and how to correctly calculate normalized irradiation. In essence, solar power forecasting requires irradiance, which is an instantaneous measure of solar radiation that can be derived from a single ground-based observation, satellite imagery, numerical weather prediction models, or other sources. Solar energy forecasting requires normalized irradiation, which is an interval measure of solar radiation measured or projected over a time period.
The distinction between irradiance and normalized irradiation is frequently overlooked, yet confusing these two solar resources is causal to photovoltaic power and energy simulation inaccuracies. The rate and quantity terms used in photovoltaic production forecasting, irradiance and normalized irradiation, are semantically related and easily mixed up, whereas other fields use linguistically distinct terms, such as transportation, which refers to rate as speed and quantity as distance. As well, irradiance and normalized irradiation reverse conventional uses of time as measures of proportionality, where rate is merely expressed as W/m2 (watts per square meter) and non-normalized quantity, that is, irradiation, is expressed as W/m2/h (watts per square meter per hour), or, in normalized form, using the same units as rate, W/m2. Using the same units for both rate and quantity contributes to confusion. Irradiance and normalized irradiation are only guaranteed to be equivalent if the irradiance is constant over the time interval over which normalized irradiation is solved. This situation rarely occurs since the sun's position in the sky is constantly changing, albeit at a very slow rate.
In addition to applying the correct type of solar resource, the physical configuration of each photovoltaic system is critical to forecasting aggregate plant power output. Inaccuracies in the assumed specifications of individual photovoltaic system configurations directly translate to inaccuracies in their power output forecasts. Individual photovoltaic system configurations may vary based on power rating and electrical characteristics and by their operational features, such azimuth and tilt angles and shading or other physical obstructions.
Photovoltaic system power output is particularly sensitive to shading due to cloud cover. As well, photovoltaic fleets that combine individual plants physically scattered over a geographical area may be subject to different weather conditions due to cloud cover and cloud speed with an effect on aggregate fleet power output. Photovoltaic fleets operating under cloudy conditions can exhibit variable and potentially unpredictable performance. Conventionally, fleet variability is determined by collecting and feeding direct power measurements from individual photovoltaic systems or indirectly-derived power measurements into a centralized control computer or similar arrangement. However, the practicality of such an approach diminishes as the number of systems, variations in system configurations, and geographic dispersion of the fleet grow.
For instance, one direct approach to obtaining high speed time series power production data from a fleet of existing photovoltaic systems is to install physical meters on every photovoltaic system, record the electrical power output at a desired time interval, such as every 10 seconds, and sum the recorded output across all photovoltaic systems in the fleet at each time interval. An equivalent direct approach to obtaining high speed time series power production data is to collect solar irradiance data from a dense network of weather monitoring stations covering all anticipated locations at the desired time interval, use a photovoltaic performance model to simulate the high speed time series output data for each photovoltaic system individually, and then sum the results at each time interval.
With either direct approach to obtaining high speed time series power production data, several difficulties arise. First, in terms of physical plant, installing, operating, and maintaining meters and weather stations is expensive and detracts from cost savings otherwise afforded through a renewable energy source. Similarly, collecting, transmitting, and storing high speed data for every location requires collateral data communications and processing infrastructure. Moreover, data loss can occur if instrumentation or data communications fail.
Second, both direct approaches only work when and where meters are pre-installed; thus, high speed time series power production data is unavailable for all other locations, time periods, and photovoltaic system configurations. Both direct approaches also cannot be used to directly forecast future photovoltaic system performance since meters must be physically present at the time and location of interest. Data also must be recorded at the time resolution that corresponds to the desired output time resolution. While low time-resolution results can be calculated from high resolution data, the opposite calculation is not possible.
The few solar data networks that exist in the United States, such as the ARM network, described in G. M. Stokes et al., “The Atmospheric Radiation Measurement (ARM) Program: Programmatic Background and Design of the Cloud and Radiation Test Bed,” Bull. of Am. Meteor. Soc., Vol. 75, pp. 1201-1221 (1994), the disclosure of which is incorporated by reference, and the SURFRAD network, lack high density networks and do not collect data at a fast rate. These limitations have prompted researchers to evaluate other alternatives, including dense networks of solar monitoring devices in a few limited locations, such as described in S. Kuszamaul et al., “Lanai High-Density Irradiance Sensor Network for Characterizing Solar Resource Variability of MW-Scale PV System.” 35th Photovoltaic Specialists Conf., Honolulu, Hi. (Jun. 20-25, 2010), and R. George, “Estimating Ramp Rates for Large PV Systems Using a Dense Array of Measured Solar Radiation Data,” Am. Solar Energy Society Annual Conf. Procs., Raleigh, N.C. (May 18, 2011), the disclosures of which are incorporated by reference. As data was collected, the researchers examined the data to determine whether there were underlying models that could translate results from these devices to photovoltaic fleet production operating over a broader geographical area, yet actual translation of the data was not provided.
In addition, satellite irradiance data for specific locations and time periods have been combined with randomly selected high speed data from a limited number of ground-based weather stations, such as described in CAISO 2011. “Summary of Preliminary Results of 33% Renewable Integration Study—2010,” Cal. Pub. Util. Comm. LTPP No. R. 10-05-006 (Apr. 29, 2011) and J. Stein, “Simulation of 1-Minute Power Output from Utility-Scale Photovoltaic Generation Systems,” Am. Solar Energy Soc. Annual Conf. Procs., Raleigh, N.C. (May 18, 2011), the disclosures of which are incorporated by reference. This approach, however, does not produce time synchronized photovoltaic fleet variability for specific time periods because the locations of the ground-based weather stations differ from the actual locations of the fleet.
The concerns relating to high speed time series power production data acquisition also apply to photovoltaic fleet output estimation. Creating a fleet power forecast requires evaluation of the irradiance expected over each location within a photovoltaic fleet, which must be inferred for those locations where measurement-based sources of historical solar irradiance data are lacking. As well, irradiance derived from satellite imagery requires additional processing prior to use in simulating fleet time series output data. Finally, simulating fleet energy output or creating a fleet energy forecast requires evaluation of the normalized irradiation expected over the period of interest. Normalized irradiation is analogous to the average irradiance measured over a time interval, yet normalized irradiation is not always available, such as in situations where only point measurements of irradiance are sporadically collected or even entirely absent.
Therefore, a need remains for an approach to facilitating accurate photovoltaic power simulation by providing an appropriate type of solar resource, regardless of whether physically measured, for use in forecasting photovoltaic fleet power and energy output.